Knowledgeable Navigation in Virtual Environments

2008 ◽  
pp. 238-245
Author(s):  
Pedram Sadeghian ◽  
Mehmed Kantardzic ◽  
Sherif Rashad

Virtual environments provide a computer-synthesized world in which users can interact with objects, perform various activities, and navigate the environment as if they were in the real world (Sherman & Craig, 2002). Research in a variety of fields (i.e., software engineering, artificial intelligence, computer graphics, human computer interactions, electrical engineering, psychology, perceptual science) has been critical to the advancement of the design and implementation of virtual environments. Applications for virtual environments are found in various domains, including medicine, engineering, oil exploration, and the military (Burdea & Coiffet, 2003). Despite the advances, navigation in virtual environments remains problematic for users (Darken & Sibert, 1996). Users of virtual environments, without any navigational tools, often become disoriented and have extreme difficulty completing navigational tasks (Conroy, 2001; Darken & Sibert, 1996; Dijk et al., 2003; Modjeska & Waterworth, 2000). Even simple navigational tools are not enough to prevent users from becoming lost in virtual environments. Naturally, this leads to a sense of frustration on the part of users and decreases the quality of human-computer interactions. In order to enhance the experience of users of virtual environments and to overcome the problem of disorientation, new sophisticated tools are necessary to provide navigational assistance. We propose the design and use of navigational assistance systems that use models derived through data mining to provide assistance to users. Such systems formalize the experience of previous users and make them available to new users in order to improve the quality of new users’ interactions with the virtual environment.

Author(s):  
Pedram Sadeghian ◽  
Mehmed Kantardzic ◽  
Sherif Rashad

Virtual environments provide a computer-synthesized world in which users can interact with objects, perform various activities, and navigate the environment as if they were in the real world (Sherman & Craig, 2002). Research in a variety of fields (i.e., software engineering, artificial intelligence, computer graphics, human computer interactions, electrical engineering, psychology, perceptual science) has been critical to the advancement of the design and implementation of virtual environments. Applications for virtual environments are found in various domains, including medicine, engineering, oil exploration, and the military (Burdea & Coiffet, 2003). Despite the advances, navigation in virtual environments remains problematic for users (Darken & Sibert, 1996). Users of virtual environments, without any navigational tools, often become disoriented and have extreme difficulty completing navigational tasks (Conroy, 2001; Darken & Sibert, 1996; Dijk et al., 2003; Modjeska & Waterworth, 2000). Even simple navigational tools are not enough to prevent users from becoming lost in virtual environments. Naturally, this leads to a sense of frustration on the part of users and decreases the quality of human-computer interactions. In order to enhance the experience of users of virtual environments and to overcome the problem of disorientation, new sophisticated tools are necessary to provide navigational assistance. We propose the design and use of navigational assistance systems that use models derived through data mining to provide assistance to users. Such systems formalize the experience of previous users and make them available to new users in order to improve the quality of new users’ interactions with the virtual environment.


In the real-time design, conceptual solving any new task is impossible without analytical reasoning of designers who interact with natural experience and its models among which important place occupies models of precedents. Moreover, the work with new tasks is a source of such useful models. The quality of applied reasoning essentially depends on the constructive use of appropriate language and its effective models. In the version of conceptual activity described in this book, the use of language means is realized as an ontological support of design thinking that is aimed at solving a new task and creating a model of corresponding precedent. The ontological support provides controlled using the lexis, extracting the questions for managing the analysis, revealing the cause-and effects regularities and achieving the sufficient understanding. Designers fulfill all these actions in interactions with the project ontology that can be developed by manual or programmed way in work with the task.


Author(s):  
Nancy Edith Ochoa Guevara ◽  
Cesar O. Díaz ◽  
Manuel Davila Sguerra ◽  
Marcelo Herrera Martinez ◽  
Oscar Acosta Agudelo ◽  
...  

With the aim of improving the citizens quality of life; the study, design anddevelopment of smart cities have been worked in different parts of the world andColombia is not excluded. Accordingly, this document presents the advances in theimplementation of a platform prototype for joining smart developments in some universities from Bogotá-Colombia. First of all, some aspects to consider in the development of a Smart City are presented. Later, the importance of virtual environments and noise studies, the drain gratings to avoid flooding by rain and the use of the bicycle as an alternative means of transport is also shown.


Author(s):  
Haibin Zhu ◽  
Ming Hou

With increased understanding of cognitive informatics and the advance of computer technologies, it is becoming clear that human-computer interaction (HCI) is an interaction between two kinds of intelligences, i.e., natural intelligence and artificial intelligence. This paper attempts to clarify interaction-related terminologies through step-by-step definitions, and discusses the nature of HCI, arguing that shared models are the most important aspect of HCI. This paper also proposes that a role-based interaction can be taken as an appropriate shared model for HCI, i.e., Role-Based HCI.


Author(s):  
Edgardo Palza ◽  
Jorge Sanchez ◽  
Guillermo Mamani ◽  
Percy Pacora ◽  
Alain Abran ◽  
...  

This chapter presents a predictive analytic model for preventing neonatal morbidity through the analysis of patterns of risky behavior regarding morbidity in newborns. The chapter presents the design and implementation of a forecasting model of Neonatal morbidity. The model developed is based on artificial intelligence using Bayesian Networks, Influence Diagrams and principles of traditional statistics. The model research is based on a repository of 10,000 medical records at a hospital in Peru. The model aims to identify the factors that are causes of morbidity in newborns, is based on data mining techniques and developed using the CRISP-DM methodology.


2020 ◽  
Vol V (I) ◽  
pp. 642-650
Author(s):  
Shabnam Gul ◽  
Muhammad Faizan Asghar ◽  
Adeel Irfan

The world has been drastically moved into new arenas by the implementation of new technologies in almost every discipline. One such advancement is Artificial Intelligence. Artificial Intelligence is playing a vital role in the military. The data scientists are designing such algorithms that can help in understanding the minds of individuals by closely analyzing the patterns of their thinking. Such Algorithms include many different approaches to data mining. All these advancements in Artificial Intelligence are assisting military forces in devising strategies that will not only enhance the functioning but will also give proactive ways rather than reactive ways while handling the wars or the threat of wars(Manzotti and Chella, 2018). In the contemporary world of using soft powers as a skill to resolve conflicts, the use of Artificial Intelligence in order to win wars through hearts and minds has been a much-needed concept. Data scientists and psychologists need to collaborate and design new algorithms to make the best use of Artificial Intelligence.


Author(s):  
Edgardo Palza ◽  
Jorge Sanchez ◽  
Guillermo Mamani ◽  
Percy Pacora ◽  
Alain Abran ◽  
...  

This chapter presents a predictive analytic model for preventing neonatal morbidity through the analysis of patterns of risky behavior regarding morbidity in newborns. The chapter presents the design and implementation of a forecasting model of Neonatal morbidity. The model developed is based on artificial intelligence using Bayesian Networks, Influence Diagrams and principles of traditional statistics. The model research is based on a repository of 10,000 medical records at a hospital in Peru. The model aims to identify the factors that are causes of morbidity in newborns, is based on data mining techniques and developed using the CRISP-DM methodology.


Author(s):  
Haibin Zhu ◽  
Ming Hou

With increased understanding of cognitive informatics and the advance of computer technologies, it is becoming clear that human-computer interaction (HCI) is an interaction between two kinds of intelligences, i.e., natural intelligence and artificial intelligence. This paper attempts to clarify interaction-related terminologies through step-by-step definitions, and discusses the nature of HCI, arguing that shared models are the most important aspect of HCI. This paper also proposes that a role-based interaction can be taken as an appropriate shared model for HCI, i.e., Role-Based HCI.


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